Focusing on the limitation of the reverse learning with random walk, a new approach is presented in [1]} for highly dynamic and unstable systems where previous method breaks down. Similar to [2]}, the backward learning method in [1]} starts from the state in the vicinity of the goal state and increases the distance when the agent demonstrates mastery in solving the problem. Unlike [2]}, where the state space expands by random walk, in [1]} the new states are obtained by computing approximate backward reachable set (BRS). BRS is the set of all points in the state space that the agent is able to reach a certain region in a fixed and short amount of time. The main contribution of this work is to update the state space in the way that only reachable states from goal state are preserved.
